A multi-feature-based intelligent redundancy elimination scheme for cloud-assisted health systems

Redundancy elimination techniques are extensively investigated to reduce storage overheads for cloud-assisted health systems.Deduplication eliminates the redundancy of duplicate blocks by storing one physical instance referenced by multiple duplicates.Delta compression is usually regarded as a complementary technique to deduplication to further remove the redundancy of similar blocks,but our observations indicate that this is disobedient when data have sparse duplicate blocks.In addition,there are many overlapped deltas in the resemblance detection process of post-deduplication delta compression,which hinders the efficiency of delta compression and the index phase of resemblance detection inquires abundant non-similar blocks,resulting in inefficient system throughput.Therefore,a multi-feature-based redundancy elimination scheme,called MFRE,is proposed to solve these problems.The similarity feature and temporal locality feature are excavated to assist redundancy elimination where the similarity feature well expresses the duplicate attribute.Then,similarity-based dynamic post-deduplication delta compression and temporal locality-based dynamic delta compression discover more similar base blocks to minimise overlapped deltas and improve compression ratios.Moreover,the clustering method based on block-relationship and the feature index strategy based on bloom filters reduce IO overheads and improve system throughput.Experiments demonstrate that the proposed method,compared to the state-of-the-art method,improves the compression ratio and system throughput by 9.68%and 50%,respectively.

DCFNet:An Effective Dual-Branch Cross-Attention Fusion Network for Medical Image Segmentation

Automatic segmentation of medical images provides a reliable scientific basis for disease diagnosis and analysis.Notably,most existing methods that combine the strengths of convolutional neural networks(CNNs)and Transformers have made significant progress.However,there are some limitations in the current integration of CNN and Transformer technology in two key aspects.Firstly,most methods either overlook or fail to fully incorporate the complementary nature between local and global features.Secondly,the significance of integrating the multiscale encoder features from the dual-branch network to enhance the decoding features is often disregarded in methods that combine CNN and Transformer.To address this issue,we present a groundbreaking dual-branch cross-attention fusion network(DCFNet),which efficiently combines the power of Swin Transformer and CNN to generate complementary global and local features.We then designed the Feature Cross-Fusion(FCF)module to efficiently fuse local and global features.In the FCF,the utilization of the Channel-wise Cross-fusion Transformer(CCT)serves the purpose of aggregatingmulti-scale features,and the Feature FusionModule(FFM)is employed to effectively aggregate dual-branch prominent feature regions from the spatial perspective.Furthermore,within the decoding phase of the dual-branch network,our proposed Channel Attention Block(CAB)aims to emphasize the significance of the channel features between the up-sampled features and the features generated by the FCFmodule to enhance the details of the decoding.Experimental results demonstrate that DCFNet exhibits enhanced accuracy in segmentation performance.Compared to other state-of-the-art(SOTA)methods,our segmentation framework exhibits a superior level of competitiveness.DCFNet’s accurate segmentation of medical images can greatly assist medical professionals in making crucial diagnoses of lesion areas in advance.

Defense Against Poisoning Attack via Evaluating TrainingSamples Using Multiple Spectral Clustering Aggregation Method

The defense techniques for machine learning are critical yet challenging due tothe number and type of attacks for widely applied machine learning algorithms aresignificantly increasing. Among these attacks, the poisoning attack, which disturbsmachine learning algorithms by injecting poisoning samples, is an attack with the greatestthreat. In this paper, we focus on analyzing the characteristics of positioning samples andpropose a novel sample evaluation method to defend against the poisoning attack cateringfor the characteristics of poisoning samples. To capture the intrinsic data characteristicsfrom heterogeneous aspects, we first evaluate training data by multiple criteria, each ofwhich is reformulated from a spectral clustering. Then, we integrate the multipleevaluation scores generated by the multiple criteria through the proposed multiplespectral clustering aggregation (MSCA) method. Finally, we use the unified score as theindicator of poisoning attack samples. Experimental results on intrusion detection datasets show that MSCA significantly outperforms the K-means outlier detection in terms ofdata legality evaluation and poisoning attack detection.

Constructing Ce-Mn/Al_(2)O_(3)with different active components for low temperature catalytic degradation of chlorobenzene in soil

Soil remediation containing numerous organic contaminants is of great significance to ecological environment.Herein,the synergetic effects of Ce-Mn/Al_(2)O_(3)with different active components on catalytic thermal desorption of chlorobenzene in soil were investigated.The optimized Ce-Mn/Al_(2)O_(3)drastically enhance the desorption efficiency of chlorobenzene,and the corresponding conversion reaches 100%within 1 h at a low temperature of 120℃.The superior performance is ascribed to the formation of Ce-Mn solid solution during the calcination process,resulting in a certain lattice change to the generation of abundant oxygen vacancies and acidic sites.Combining with the analysis of in-situ diffuse reflectance infrared spectroscopy and gas chromatography-mass spectrometry,the final products of chlorobenzene are decomposed into CO_(2),H_(2)O,Cl_(2)and HCl.This work sheds light on the rational design of highly-active catalysts for practical applications of sustainable soil remediation.

转移灶不可切除的无症状结直肠癌是否行原发灶肿瘤切除术的Meta分析

目的评估原发灶肿瘤切除(PTR)加化疗对比单纯化疗治疗转移灶不可切除无症状结直肠癌患者的疗效。方法检索从数据库建立至2022年10月发表在PubMed、Embase、Web of science、Cochrane Library、中国知网、万方以及维普等数据库中的相关研究,采用Review Manager 5.4软件以及Stata 16.0软件进行生存资料的Meta分析。以HR及其95%CI来评估患者的生存风险,采用Q检验及I^(2)检验来评估研究之间异质性大小,采用敏感性分析评估结果的可靠性,采用亚组分析寻找异质性的来源,采用Egger's检验以及漏斗图评估研究之间发表偏倚。结果共纳入10项研究,包括5项随机对照试验(RCT)和5项回顾性队列研究(RCS)。Meta分析结果显示患者总生存率(OS)和无进展生存期(PFS)在两种治疗方式之间差异无统计学意义;亚组分析的结果同样提示PTR并没有给患者带来额外的生存获益。漏斗图以及Egger's检验的结果(t=-0.360,P=0.730)表明报道患者OS的研究无明显发表偏倚。结论PTR加化疗相对于单纯化疗不能给患者带来更多的生存获益,但鉴于本研究的局限性,仍需要后续大样本随机对照试验进行验证。

Resource utilization of high concentration SO_(2) for sulfur production over La-Ce-O_(x)@ZrO_(2) composite oxide catalyst

Sulfur dioxide is one of the main causes of air pollution such as acid rain and photochemical smog,and its pollution control and resource utilization become an important research direction of air pollution control,The active component La-Ce-O_(x) is loaded on SiO_(2),γ-Al_(2)O_(3),TiO_(2) and ZrO_(2),and the La-Ce-Ox@ZrO_(2)exhibits the best catalytic activity.By adjusting the loading amount of La-Ce-O_(x),La-Ce-Ox@ZrO_(2) with different mass fractions was prepared.The results show that the activity of 15%La-Ce-Ox@ZrO_(2)catalyst is the best.The SO_(2)conversion is 100%,and the S yield and S selectivity are more than 96% at 350℃.According to the analysis results of H_(2)-TPR,CO_(2)-TPD and NH_(3)-TPD,ZrO_(2) as a support not only reduces the acidity of the catalyst,but also improves the weak alkaline sites of the catalyst,which is conducive to the adsorption and activation of SO2molecules at low temperature.The incorporation of La and Ce increases the oxygen concentration adsorbed on the catalyst.The strong interaction between the support ZrO_(2) and the active component La-Ce-Oxis conducive to the electron transfer between the active component and the support,and improves the activity of the catalyst.For the 15%La-Ce-O_(x)@ZrO_(2),the main reaction intermediates are weakly adsorbed SO_(2)(SO_(3)^(2-)),bicoordinated CO_(3)^(2-),monodentate carbonate and CO in the gas phase.Therefore,the catalytic reaction follows both L-H and E-R mechanisms.